The usefulness of video surveillance systems is becoming increasingly acknowledged as the demand for enhanced safety has increased, both for fixed locations and properties and for moving vehicles. Locations commonly covered by such systems, include, for example, monitoring of harbors, airports, bridges, power plants, parking garages, public spaces, and other high-value assets. Traditionally, such camera networks require a labor-intensive deployment and monitoring by human security personnel. Human-monitored systems are, in general, relatively costly and prone to human error. For these reasons, the development of technology to automate the deployment, calibration, and monitoring of such systems will be increasingly important in the field of video surveillance. Moving vehicles, as well, would benefit from the greater safety provided by video surveillance, which could, for example, alert the driver of a passenger car of a foreign object in the road, or in the case of a driverless vehicle, alter the planned route because of a detected object or obstacle in the planned route.
For example, in automated video surveillance of sensitive infrastructures, it is always desirable to detect and alarm in the event of intrusion. To perform such a task reliably, it is often helpful to classify detected objects beforehand and to also track such detected objects in an attempt to discern from their actions and movements whether the objects pose an actual threat. Detecting an object is no easy task, however. It requires powerful video analytics and complex algorithms supporting those analytics. For example, a pixel in an aligned image sequence, such as a video stream, is represented as a discrete time series. The states of that time series are not observable, but, through an abstraction operation, they do aid in the observation of the state of the pixel at a given time, which depends on the immediately prior state, and not necessarily on earlier states, much as in the way a Hidden Markov Model is used. It often requires determining which portions of a video or image sequence are background and which are foreground, and then detecting the object in the foreground. Further, object detection is complicated when the camera imaging the target moves, either because it is mounted to something which is mobile or because the camera is monitoring a wide field of view by a step-and-stare method of camera movement.
Generally, the video surveillance system is unable to determine the actual size of an object, which can make threat detection even more difficult. With actual size detection, benign objects can be better differentiated from real threats. Moreover, the kinematics of an object, such as its velocity, acceleration, and momentum, are much more difficult to analyze when real size is unknown.
Additionally, georeferencing with a single camera demands the existence of landmark-rich scenes which may not be available in many instances, such as in the surveillance of ports and harbors, or when a site is being remotely—and perhaps covertly—monitored, and it is not feasible to introduce synthetic landmarks into the scene. Clearly, in reference to the above-described issues, the development of systems to improve the efficiency and effectiveness of automated video surveillance is needed.